摘要
为实现电动汽车用轮毂电机运行状态在线监测及其安全评估,提出一种基于动态贝叶斯网络(dynamic Bayesian networks,简称DBNs)的轮毂电机机械故障在线诊断方法。首先,以轮毂电机运行安全为目标,着重考虑车速对轮毂电机振动信号的影响程度,在时域和频域中提炼出多个敏感度高的特征参数来表征轮毂电机的运行状态,并将其作为DBNs的观测节点;其次,基于速度片构建轮毂电机机械故障诊断模型,解决其运行状态在相邻时间片之间无法构建转移概率分布的问题,根据不同速度片之间的转移概率分布,建立以二速度片展开的DBNs,实现对轮毂电机机械故障的在线诊断;最后,通过轮毂电机综合台架试验,验证了该方法对轮毂电机机械故障在线诊断的有效性。
To realize online monitoring and safety assessment of in-wheel motor running state for electric vehicles,an online method for mechanical fault diagnosis of in-wheel motor based on dynamic Bayesian networks(DBNs)is proposed in this paper.Multiple parameters highly sensitive to common mechanical faults are chosen as the characterization of in-wheel motor running state and observed nodes of DBNs under the consideration of the effects of vehicle speed on vibration signals of in-wheel motor in time domain and frequency domain for the safety of in-wheel motor running state.To solve the problem that the state transition probability distribution cannot be constructed between two continuous"time slices"of in-wheel motor running state,the mechanical fault diagnosis models for online mechanical fault diagnosis can be established based on two continuous"speed slices"with the state transition probability distributions between the different speeds.Finally,the effectiveness of this method is verified by the experiments of in-wheel motor test bench.
作者
李仲兴
陈震宇
薛红涛
殷苏群
江洪
LI Zhongxing;CHEN Zhenyu;XUE Hongtao;YIN Suqun;JIANG Hong(School of Automotive and Traffic Engineering,Jiangsu University Zhenjiang,212013,China;School of Mechanical Engineering,Jiangsu University Zhenjiang,212013,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2020年第4期643-649,818,819,共9页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51775245,51575241)
江苏省重点研发计划资助项目(BE2017129)。
关键词
轮毂电机
机械故障
在线诊断
动态贝叶斯网络
in-wheel motor
mechanical fault
online diagnosis
dynamic Bayesian networks
